Download Gastrointestinal microorganisms in cats and dogs: a brief

Arch Med Vet 45, 111-124 (2013)
REVIEW ARTICLE
Gastrointestinal microorganisms in cats and dogs: a brief review
Microorganismos gastrointestinales en gatos y perros: una revisión breve
JF Garcia-Mazcorroa*, Y Minamotob
a
b
Facultad de Medicina Veterinaria y Zootecnia, Universidad Autónoma de Nuevo León, Nuevo León, México.
Gastrointestinal Laboratory, Department of Small Animal Clinical Sciences, Texas A&M University, Texas, USA.
RESUMEN
El tracto gastrointestinal (GI) de animales contiene diferentes tipos de microorganismos conocido como la microbiota GI. Por mucho tiempo,
la microbiota GI ha generado interés porque los microorganismos GI están involucrados en múltiples procesos fisiológicos en el hospedero, así
perpetuando salud o enfermedad. Estudios recientes han demostrado que la microbiota GI de gatos y perros es tan compleja como en humanos y otros
animales, revelado con el uso de tecnologías de secuencia modernas y otras técnicas moleculares. La microbiota GI incluye miembros de todos los tres
dominios principales de vida (Archaea, Bacterias y Eucariotas), pero las bacterias son el grupo de microorganismos más abundante y metabólicamente
activo. El estómago de gatos y perros esta principalmente poblado de Helicobacter spp., el cual en perros puede representar tanto como el 98% de
toda la microbiota bacteriana en el estómago. El intestino delgado contiene una microbiota más diversa, conteniendo representantes de al menos cinco
diferentes filos bacterianos (principalmente Firmicutes y Bacteroidetes). El intestino grueso contiene el grupo de bacterias más abundante (~1011 células
bacterianas por gramo de contenido intestinal), diverso (al menos diez diferentes filos han sido detectados) y metabólicamente relevante en el tracto
GI. La mayoría de las bacterias en el intestino grueso son anaerobios estrictos, los cuales dependen de la fermentación de sustancias no digeridas para
subsistir. Aunque estudios recientes han dilucidado las complejidades de la microbiota GI en gatos y perros, más investigación todavía es necesaria para
encontrar maneras de manipular exitosamente los microorganismos GI para prevenir y/o tratar enfermedades GI.
Palabras clave: gastrointestinal, microbiota, gatos, perros.
SUMMARY
The gastrointestinal (GI) tract of animals contains different types of microorganisms known as the GI microbiota. The GI microbiota has long been
of interest because of its involvement in multiple physiological processes in the host, influencing health or disease. Recent studies have shown that the
GI microbiota of cats and dogs is as complex as the one present in humans and other animals, according to state-of-the-art sequencing technologies
and other molecular techniques. The GI microbiota includes members of all three main life domains (Archaea, Bacteria, and Eukaryotes), with bacteria
being the most abundant and metabolically active group of microorganisms. The stomach of cats and dogs is mainly inhabited by Helicobacter spp.,
which in dogs may account for as much as 98% of all gastric bacterial microbiota. The small intestine harbors a more diverse microbiota as it contains
representatives from at least five bacterial phyla (mainly Firmicutes and Bacteroidetes). The large intestine harbors the most abundant (~1011 bacterial
cells per gram of intestinal content), diverse (at least 10 bacterial phyla have been identified) and physiologically relevant group of bacteria in the GI
tract. Most bacteria in the large intestine are strict anaerobes that depend on fermentation of non-digested dietary substances to subsist. Although recent
studies are shedding light into the complexity of the GI microbiota in cats and dogs, further research is needed to find ways to successfully manipulate
GI microorganisms to prevent and/or treat GI diseases.
Key words: gastrointestinal, microbiota, cats, dogs.
INTRODUCTION
The gastrointestinal (GI) tract of animals is colonised by a dense and heterogeneous group of microorganisms known as the GI microbiota, which supply more
than nine million unique genes to the gene repertoire in
the eukaryotic host (Yang et al 2009). The GI microbiota
has long been of interest because of its involvement in
multiple physiological processes in the host, including
Accepted: 10.01.2013.
* Francisco Villa s/n Col. Ex-Hacienda El Canadá C.P. 66050, Gral.
Escobedo, Nuevo León, México; [email protected]
resistance against colonization by pathogens (Stecher
and Hardt 2011), production of useful substances that act
as energy source for intestinal epithelial cells (Louis and
Flint 2009), modulation of the intestinal immune system
(Hooper and Macpherson 2010), salvage of energy from
undigested dietary components (Cummings and Macfarlane 1997), and stimulation of intestinal angiogenesis
(Stappenbeck et al 2002).
Most of the current information about the composition and activity of the GI microbiota comes from studies
in human populations. However, an increasing number
of investigations have also studied intestinal microbes
in other animals, especially cats and dogs (Suchodolski 2011). This review summarises current information
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JF GARCIA-MAZCORRO Y MINAMOTO
about the GI microbiota with emphasis on the GI bacterial microbiota of cats and dogs.
THE GI MICROBIOTA
The GI tract of animals is one of the most complex
microbial ecosystems on Earth, and it is continuously
affected by factors associated with the host (Spor et al
2011, Van den Abbeele et al 2011) and the outside environment (Claesson et al 2012). This complexity has
been an obstacle to study single independent factors associated with its changes over time and among different
populations of animals (e.g., healthy and diseased). Also,
it is often difficult to determine the nature of the interactions among the microorganisms during health or disease, although recent advances in mathematical modeling
could help understand this phenomenon (Hellweger and
Bucci 2009, Arciero et al 2010). Moreover, there are controversies with regards to the way we classify microbial
species (Staley 2006, Schleifer 2009). Despite this complexity, there is a growing body of literature suggesting
that the GI microbiota can be studied objectively, and that
health could be enhanced in the host through manipulation of its constitutive intestinal microbial populations.
CHARACTERIZATION OF THE GI MICROBIOTA
CULTURE METHODS
The characterization of the GI microbiota is the first
step in determining its role in health or disease. Classic
culture methods have the advantages of being relatively
inexpensive, widely available, and suitable for biochemical and physiological studies, and therefore have been
extensively used to characterise the GI microbiota of
cats and dogs (see below). However, the usefulness of
culture techniques to characterise microorganisms in the
gut and elsewhere has long been questioned because it
is not representative enough regarding both enumeration
and community structure (Ritz 2007). While experts generally agree that about 99% of all GI microorganisms
have not been successfully cultured (Tap et al 2009), a
recent article showed that about 70% of all fecal bacterial genera (as determined by pyrosequencing) could be
successfully cultured using an in-house culture media
containing a mixture of several commercially available
ingredients (Goodman et al 2011). Modifications to this
universal gut microbiota media will facilitate the culture
of more intestinal microorganisms and make possible a
correlation between microbial abundance and utilization
of dietary substances.
MOLECULAR METHODS
In contrast to culture methods, which rely on the identification of GI microorganisms by means of a phenoty112
pic characterization, molecular methodologies aim to
identify and categorise microorganisms by means of detecting specific molecules inside the cells (e.g., DNA or
RNA) (Zuckerkandl and Pauling 1965). The 16S rRNA
gene has often been used to identify bacteria because it
is universally distributed and appears to have undergone
a relatively slow change in base pair composition throughout evolution (Fox et al 1980). In other words, the 16S
rRNA gene contains conserved regions (same among all
bacteria) as well as variable and highly variable regions
that allow the distinction and classification of bacterial
phylotypes, according to theories of molecular evolution
(Lemey et al 2009). Some examples of methodological
differences between culture-based and culture-independent approaches include survey depth (tens to hundreds
of cultural isolates versus thousands to millions of 16S
rRNA gene sequences), accuracy of bacterial 16S rRNA
gene assignments, and documentation of the generated
data (Goodman et al 2011). A summary of the most commonly used methods and techniques to study the gastrointestinal microbiota is presented in table 1.
Polymerase chain reaction (PCR). PCR is a common and
often indispensable molecular technique to characterize
the GI microbiota. Currently, PCR is performed using
a heat-stable DNA polymerase which can generate millions of copies of a given target sequence (e.g., a 16S
rRNA gene fragment) in one hour or less. Some sequencing techniques require the use of PCR for generating
amplicons (i.e., DNA fragments amplified by PCR). It
is important to note that all PCR-based techniques suffer
from several biases, including the fact that the generated
16S rRNA gene copies cannot be accurately extrapolated to the number of the microorganisms themselves, in
part because different bacteria have different number of
copies of this gene even within the same species (Acinas
et al 2004, Lee et al 2008). Interestingly, these differences in the number of copies of the 16S rRNA gene may
reflect ecological strategies of bacteria in respond to resource availability (Klappenbach et al 2000).
Fingerprinting methods. The obtained 16S rRNA gene
amplicons (e.g., from intestinal contents) are often the
same size in number of base pairs, and therefore would
appear as a single band in an agarose or polyacrylamide
gel. However, these amplicons are likely to differ from
one another in their base pair composition. When exposed to a denaturing agent or to increasing temperatures,
these differences in base pair composition make the amplicons migrate at a different speed throughout a gel matrix. Denaturing Gradient Gel Electrophoresis (DGGE)
and Temperature Gradient Gel Electrophoresis (TGGE)
are examples of molecular fingerprinting methods that
separate amplicons based on this principle. In particular,
DGGE has been shown to be useful to assess qualitative
variations in the GI microbiota of dogs among different
Target
Microorganisms
Specific genes
Specific genes
Specific genes
Specific genes
Specific genes
Specific genes
Specific genes
Specific genes
Specific genes
All DNA of a
microorganism
All genes in a sample
All proteins in a sample
Method/Technique
Culture
PCR
qPCR
DGGE/TGGE
TRFLP
Sanger Sequencing
Pyrosequencing
Illumina
ION Torrent
Microarrays
Whole genome
sequencing
Metagenomics
Proteomics
Provides a view of metabolic activity
Provides a view of community structure
and metabolic potential
Provides a view of metabolic potential
and a better phylogenetic resolution
High throughput; fast and reproducible
High throughput; per-base accuracy
of 99.6% within the first 50 bases and
98.9% within the first 100 bases
Higher throughput than
454-pyrosequencing with a similar
accuracy
High throughput with an accuracy of
more than 99.9%
Widely available
Relatively fast and low-cost
Relatively fast and low-cost
Allows quantification
Low-cost
Low-cost and allows for phenotypic
characterization
Main Advantage (s)
Rothberg et al (2011)
Pettersson et al (2009)
Caporaso et al (2012)
Luo et al (2012)
Ronaghi et al (1996)
Margulies et al (2005)
Kunin et al (2010)
Sanger et al (1977)
Liu et al (1997)
Egert and Friedrich (2003)
Nikolausz et al (2005)
Klein (2002)
Mackay (2004)
Rådström et al (2004)
Ritz (2007)
Goodman et al (2011)
Useful References
Similar to high-throughput sequencing technologies,
the depth of analysis affects the coverage of all
coding capacity of the gut microbiota
The total amount of sequencing required to sequence
all microbial genomes in a sample is unfeasible
The total amount of sequencing required to sequence
all microbial genomes in a sample is unfeasible
Wilkins et al (1996)
Kolmeder et al (2012)
Huson et al (2009)
Preidis and Versalovic (2009)
Hugenholtz and Tyson (2008)
Bentley (2006)
Fails to detect microbial sequences that were not
Rajilic-Stojanovic et al (2009)
represented in the reference sequences used for probe Van den Bogert et al (2011)
design
When the paper was published in 2011, only 20-40%
of the sensors in a given run yielded mappable reads
Shorter read length (150 bases for the MiSeq) than
454-pyrosequencing
Similar to other high-throughput sequencing
technologies, errors during the sequencing procedure
could artificially inflate diversity estimates
Low throughput
Secondary restriction fragments
PCR amplicons often do not separate well
It is inaccurate to extrapolate gene copies to bacterial
cell numbers
Qualitative (presence or absence of the gene)
Time consuming and not representative
Main Disadvantage (s)
Resumen de los métodos y técnicas mas comúnmente usados para estudiar la microbiota gastrointestinal.
Table 1.Summary of the most commonly used methods and techniques to study the gastrointestinal microbiota.
GASTROINTESTINAL, MICROBIOTA, CATS, DOGS
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compartments of the intestinal tract (Suchodolski et al
2005). The result of DGGE or TGGE analysis is a specific banding pattern for every sample analysed, therefore
providing a qualitative view of the microbial composition
of the sample. However, fingerprinting techniques are not
very useful to characterise microbial ecosystems because they often fail to accurately separate 16S rRNA gene
fragments (Jackson et al 2000, Nikolausz et al 2005),
thus underestimating the true bacterial diversity and its
changes against external perturbations.
Quantitative real-time PCR (qPCR). In conventional
PCR, the amplicons are routinely detected using electrophoresis on agarose gels after the PCR has finished.
Because of this, traditional PCR is not capable of quantifying the genomic targets; it only provides information
about the presence (band in the gel) or absence (no band
in the gel) of the target. In contrast, PCR has been adapted
to also allow the quantification of the unknown genomic
targets as the PCR progresses (in real-time). This is possible by including in the PCR reaction a fluorescent molecule that reports an increase in the amount of DNA with
a proportional increase in fluorescent signal. The fluorescent chemistries employed for this purpose include DNAbinding dyes and fluorescently-labeled sequence-specific
primers or probes. qPCR has been widely used to assess
the effect of different treatments on the abundance of the
GI microbiota in cats and dogs (Gronvold et al 2010, Garcia-Mazcorro et al 2011) as well as in humans (Malinen
et al 2005, Larsen et al 2011). However, bacterial cell
numbers cannot directly be estimated from qPCR data in
part because the cellular genome content varies with the
growth phase of the organisms and bacteria have different
number of copies of the 16S rRNA gene (see PCR above).
Fluorescence in situ hybridization (FISH). The detection
of bacterial genomic targets using qPCR is useful when
evaluating changes in the quantitative abundance of the microbiota, for example during administration of probiotics
or therapeutic agents. However, the accurate extrapolation
from amplified genomic targets to the actual numbers of
bacterial cells is often difficult, mainly because bacteria
have different copy numbers of the 16S rRNA gene. Unlike
qPCR, FISH is capable of quantifying the actual bacterial
cells by direct labeling of the 16S rRNA using fluorescently-labeled oligonucleotides. The FISH technique takes
advantage of the fact that each bacterium usually contains
thousands of ribosomes spread throughout the cell. Theoretically, like in PCR, it is possible to develop oligonucleotides that are capable to detect microorganisms at all taxonomic levels (i.e., Phylum, Class, Order, Family, Genus).
However, this is often challenging due to high similarities
in the 16S rRNA gene composition among phylogenetically
related microorganisms. FISH can also provide important
information about the morphology and spatial distribution
of microorganisms in the GI tract (Simpson et al 2006).
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Sequencing technologies. The identity of each 16S rRNA
gene amplicon can be determined by estimating the order of the base pairs (sequencing). This has been traditionally done using nucleotides base analogs (dideoxynucleotides) that lack the 3’-hydroxil group essential in
phosphodiester bond formation, which act as specific
chain-terminating inhibitors of DNA polymerase (Sanger et al 1977). This Sanger method is still used routinely in many laboratories for sequencing low number
of samples. However, complex microbial ecosystems
such as the intestinal tract contain millions of microorganisms, which makes necessary to clone and sequence
thousands of individual PCR amplicons in order to obtain a representative view of the microbial composition.
Recently developed high-throughput techniques such as
454-pyrosequencing (Margulies et al 2005) are capable
of sequencing millions of base pairs in one hour or less,
and have shown to be useful to study the feline and canine GI microbiota (Suchodolski et al 2009, Middelbos
et al 2010, Garcia-Mazcorro et al 2011, Handl et al 2011).
Other high-throughput techniques are based on different
principles (e.g., reverse termination) and are discussed
elsewhere (Pettersson et al 2009). Interestingly, a nonoptical genome sequencing has been developed (Rothberg et al 2011), which promises a better performance
than traditional optical-based sequencing. Nonetheless,
the cost and necessary expertise for both sequencing and
after-sequencing analysis procedures make most of these techniques inaccessible for many scientists around the
globe. Fortunately, a number of freely available software
platforms have been developed such as QIIME (Quantitative Insights into Microbial Ecology1), which is capable of
analyzing thousands of sequences in short periods of time.
QIIME also offers free comprehensive guides for beginners as well as expert advice for more advanced users.
THE COMPOSITION OF THE GI MICROBIOTA
The composition and metabolic activity of the GI microbiota varies along the GI tract, in part reflecting anatomical and physiological conditions inherent to each of
the intestinal sections. In cats and dogs, as well as in other
monogastric animals, both the bacterial diversity (an index
that incorporates the number of species in an area and their
relative abundance) and richness (number of species) are
higher in the large intestine when compared to the stomach
and all regions of the small intestine (Ritchie et al 2008, Suchodolski et al 2008). The GI microbiota includes all three
major domains of life (Archaea, Bacteria, and Eukaryotes),
but bacteria make up the most abundant and metabolically
active group of microorganisms in the GI tract. For example, a recent metagenomic study showed that bacteria may
represent as much as 98% of all fecal microbiota in dogs,
with Archaea, Eukaryotes, and viruses representing only
1
http://www.qiime.org/
GASTROINTESTINAL, MICROBIOTA, CATS, DOGS
about 2% (Swanson et al 2011). Similarly, a recent study
also used a metagenomic approach and showed that Eukaryotes, Archaea, and viruses were minor constituents (< 3%)
of the fecal microbiota in cats, while bacteria represented
the great majority (97.8%) (Tun et al 2012).
In monogastric animals, the large intestine contains
the most abundant, diverse and metabolically relevant
group of bacteria in the GI tract. The large intestine contains bacterial groups mainly within the phyla Firmicutes
and Bacteroidetes. Other phyla such as Actinobacteria,
Proteobacteria, Fusobacteria, Spirochaetes, Verrucomicrobia, Cyanobacteria, and Tenericutes are also frequently identified but their proportions are usually low. However, the exact proportions of each bacterial group vary
widely throughout the literature. For example, one study
showed that healthy cats and dogs may harbor > 90%
of Firmicutes in faeces (Handl et al 2011), while others
have shown that these animal species may only harbor
~ 13% (cats) and ~ 35% (dogs) of this phylum also in
faeces (Swanson et al 2011, Tun et al 2012). The reasons
for these discrepancies (see below) are unknown but may
include differences in DNA extraction protocols (Zoetendal et al 2001), intra-stool variability of intestinal microorganisms (Garcia-Mazcorro et al 2009), inter-individual
differences (Handl et al 2011), the target region of the
16S rRNA gene (Baker et al 2003), as well as inherent
differences among the techniques utilized to characterize
the microbiota (Zoetendal et al 2004, Kunin et al 2010).
Early culture-based studies suggested that the distal part
of the human intestinal tract may harbor about 300 different bacterial species (Moore and Holdeman 1975, Savage
1977). However, recent culture-independent studies suggest that on average humans have an estimated richness of
943 bacterial species (operational taxonomic units or OTUs
at 98% similarity) in faeces per subject (Tap et al 2009). In
contrast, one study suggested that cats and dogs may harbor
only 60 (cats) and 39 (dogs) OTUs (97% similarity) in faeces per subject (Handl et al 2011). This agrees with other
studies that showed the presence of only 84 and 52 OTUs
in the colon of cats and dogs based on a 98% similarity criterion (Ritchie et al 2008, Suchodolski et al 2008).
THE GI MICROBIOTA OF CATS AND DOGS
An overview of some of the most relevant investigations of the GI microbiota in cats and dogs is presented
in table 2. Among all regions of the GI tract of these and
other animal species, the distal part of the intestinal tract
(i.e. fecal microbiota) has been the most widely studied
to date (figure 1), mainly because of the ease of sampling.
Figure 1.Simplified view of the faecal bacterial composition of dogs (A, left) and cats (B, right) at phylum, order and genus level.
Numbers represent the average (minimum-maximum) of the relative proportions of sequences (number of sequences obtained from
the bacterial group divided by the total number of sequences obtained), calculated according to both a published (Garcia-Mazcorro et
al 2011, 12 dogs and 12 cats) and an unpublished (Weber et al 10 dogs and 10 cats) study using 454-pyrosequencing with the same
primer set. Clostridium was the most abundant genus in both cats and dogs ( > 20% on average in both studies) but it does not appear
in this figure due to uncertain taxonomic classification. At the phylum level, we also included the approximate estimates (*) published
by Swanson et al (2011) and Tun et al (2012) using a metagenomics approach (please see main text for more details).
Visión simplificada de la composición bacteriana fecal en perros (A, izquierda) y gatos (B, derecha) al nivel de filo, orden y género. Los números
son promedios (mínimo-máximo) de proporciones relativas de secuencias (número de secuencias obtenidas del grupo bacteriano dividido entre el número
total de secuencias obtenidas) calculado de un estudio publicado (García-Mazcorro et al 2011, 12 perros y 12 gatos) y un estudio no publicado (Weber et al
10 perros y 10 gatos) usando 454-pirosecuenciacion con el mismo par de oligonucleótidos. Clostridium fue el género más abundante en gatos y perros
(> 20% en promedio en ambos estudios) pero fue omitido en esta figura debido a clasificación taxonómica incierta. Al nivel de filo, también se incluyeron
los estimados aproximados (*) publicados por Swanson et al (2011) y Tun et al (2012) usando un método metagenómico (ver texto para más detalles).
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Dog
Dog
Dog
Cat
Dog
Postnatal changes in the
GI microbiota
Diagnostic yield of
routine fecal panel
Characterization of the
GI microbiota
Characterization of the
GI microbiota
Inflammatory Bowel
Disease (IBD)
9 (healthy) and 10
(diseased)
4 (healthy) and 1
(specific pathogenfree)
6 (healthy)
177 (healthy) and 260
(diarrhea)
110 (healthy)
18 (healthy)
Cat
10 (healthy) and 17
(diseased)
Dog
Effect of age, breed and
fiber
58 (healthy), 32
(hospitalized with
diarrhea), and 42
(hospitalized without
diarrhea)
IBD
Dog
Enteric pathogens
16 (healthy)
Number of subjects
64 (healthy) and 71
(diseased)
Dog
Post-natal changes in the
GI microbiota
Fungal DNA in the small Dog
intestine
Animal species
Topic
A significant association was found for
the presence of diarrhea and detection
of CPE or toxin A via ELISA for C.
perfringens and C. difficile
First study characterizing genotype
and phenotype of two potentially
pathogenic bacteria
Buddington (2003)
Canine IBD is associated with altered
duodenal microbial communities
Cats harbor a higher microbial
diversity than previously thought
Dogs harbor a higher microbial
diversity than previously thought
Xenoulis et al (2008)
Ritchie et al (2008)
Suchodolski et al (2008)
The diagnostic value of a fecal panel in Cave et al (2006)
dogs with diarrhea appears to be low
Age-related changes in the GI
microbiota coincide with changes in
diet and physiological processes
Simpson et al (2002)
Marks et al (2002)
Benno et al (1992)
Reference
First study to evaluate the
relationship between mucosal
bacteria and IBD in cats
(continued)
(continuación)
Janeczko et al (2008)
IBD is associated with changes
in the small intestine microbiota,
abnormalities in mucosal architecture,
immune upregulation and clinical signs
First study showing fungal
High prevalence and diversity of fungal Suchodolski et al (2008)
diversity with molecular techniques DNA in the canine small intestine
in the canine small intestine
First study to evaluate microbiota
of the small intestine in dogs with
IBD
First study to analyze different
regions of the feline GI tract using
molecular techniques
First study to analyze different
regions of the canine GI tract using
molecular techniques
First study evaluating the
diagnostic value of presence or
absence of microbes in feces of
dogs
Most comprehensive study
describing postnatal changes in the
canine GI microbiota
First study using DGGE to evaluate Individual dogs have a unique and
canine fecal microbiota
stable fecal microbiota
Advances in age of beagle dogs
yielded some changes in the
microbiota of the large bowel
Study main conclusion (s)
First study evaluating age-related
differences in the canine GI
microbiota
Highlights
Visión general de algunas de las investigaciones más relevantes acerca de la microbiota gastrointestinal (GI) en gatos y perros.
Table 2. Overview of some of the most relevant investigations about the gastrointestinal (GI) microbiota in cats and dogs.
JF GARCIA-MAZCORRO Y MINAMOTO
Cat
Cat/dog
Dog
Dog
Dog
Cat
Dog
Characterization of the
GI microbiota
Effect of synbiotic
Metagenomics
Effect of a proton-pump
inhibitor
Short-term temporal
variability
Metagenomics
Fecal microbiome
First study using metagenomics
to characterize the feline fecal
microbiota
First study using different
molecular techniques to investigate
temporal variability in fecal
microbiota of dogs
First study evaluating the effect of
a PPI on the canine GI microbiota
First study using metagenomics
to characterize the canine fecal
microbiota
First study using pyrosequencing
to evaluate the effect of a synbiotic
on fecal microbiota of cats and
dogs
One of the first studies evaluating
the feline fecal microbiota, first
using cpn60 gene (encoding the
universally conserved 60 kDa
chaperonin)
32 (healthy), 12 (acute Most comprehensive study of the
fecal microbiome in dogs with
non-hemorrhagic
intestinal disease
diarrhea), 13 (acute
hemorrhagic diarrhea),
9 and 10 (active
and controlled IBD,
respectively)
5 (healthy)
6 (healthy)
8 (healthy)
6 (healthy)
24 (healthy)
5 (indoor) and 4
(outdoor, predatory)
all healthy
Garcia-Mazcorro et al (2011)
The administered synbiotic led to
increases in probiotic bacteria in
feces without modifying the overall
phylogenetic composition of the fecal
microbiota
Gastrointestinal disorders are
associated with bacterial dysbiosis in
feces of dogs. The observed changes
differed between acute and chronic
disease states.
Suchodolski et al (2012)
Tun et al (2012)
Garcia-Mazcorro et al (2012b)
Different molecular techniques give
different views of the fecal microbial
composition in dogs
Cat metagenome clustered with
the chicken metagenome both
phylogenetically and metabolically
Garcia-Mazcorro et al (2012ª)
Omeprazole leads to quantitative
disturbances on specific groups of the
GI microbiota in dogs
Mice, humans, and dogs share
Swanson et al (2011)
phylogenetic and metabolic similarities
Desai et al (2009)
Substantial animal-animal variation,
Bifidobacterium spp. seems to be
highly abundant in feces of cats, based
on the cpn60 gene
GASTROINTESTINAL, MICROBIOTA, CATS, DOGS
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It is important to keep in mind that there are important
differences in the reported abundances of GI microorganisms among different studies. This may be due to the
DNA extraction method employed as well as the number
of copies and the target region within the 16S rRNA gene
(see above). A good example of these discrepancies is the
recent summary of Armougom and Raoult (2008) about
Firmicutes and Bacteroidetes in humans and mice.
THE GI MICROBIOTA OF CATS
Stomach. The stomach of animals was traditionally
thought to lack a complex microbial ecosystem. This belief was sustained in part by the observation that gastric
acid kills several microorganisms instantly (Giannella et
al 1972). However, culture-independent molecular techniques are revealing a different story. An early study used
several detection techniques and showed that the stomach
of 91% of pet cats (n=58) were positive for the genus
Helicobacter (Neiger et al 1998), suggesting a high occurrence of this bacterial group in the feline stomach. In
another study, it was shown that cats and dogs are predominantly coloniszed by H. heilmannii (Priestnall et al
2004) but other species (e.g., H. felis, H. bizzozeronii, H.
salomonis, H. pametensis) have also been identified in
these animal species (Neiger and Simpson 2000).
Small intestine. Osbaldiston and Stowe (1971) were
among the first to investigate the composition of the GI
microbiota in cats (n = 12) using a wide variety of culture
media. In this study, coliforms, Streptococcus, Enterococcus, and Lactobacillus were the predominant groups
of bacteria along the feline GI tract. Other earlier studies
suggested that Bacteroides and Clostridium spp. were the
most common bacteria in the duodenum of cats (Papasouliotis et al 1998, Johnston et al 2001), also based on
cultural isolates. Similarly, a recent study used molecular
techniques and suggested that the small intestine (i.e.,
jejunum) of cats harbors mainly the orders Clostridiales
and Lactobacillales (~ 90%) but also small proportions of
at least five more orders, while the ileum and the colon
both harbored a high proportion ( > 50%) of Clostridiales
with low proportions of Actinobacteria (~ 5%) (Ritchie
et al 2008).
Large intestine. A recent study sequenced the gene encoding the universal 60 kDa chaperonin and showed that the
faecal microbiota of cats was dominated by Actinobacteria (~ 53%) and Firmicutes (~ 40%) (Desai et al 2009).
Recent studies using 454-pyrosequencing suggest that >
90% of all sequences obtained from feces of healthy cats
belong to the Phylum Firmicutes (Garcia-Mazcorro et al
2011, Handl et al 2011), especially members of the family Clostridiaceae, while a metagenomic study suggests
that Bacteroidetes/Chlorobi is the most abundant bacterial group (~ 68%), followed by Firmicutes (~ 13%) and
118
Proteobacteria (~ 6%) also in faeces of cats (Tun et al
2012). However, it is important to point out that the study
by Tun et al also reports that Bacteroidetes only represented ~ 9% of the obtained sequence reads, while the
phyla Chlorobi and Chloroflexi represented less than 1%
of the reads. Thus, it is not clear which group represented
the remaining ~ 59% of difference between the reported
percentage of the Bacteroidetes/Chlorobi group (~ 68%)
and the reported percentage of Bacteroidetes alone (~
9%). We speculate that these discrepancies in the reported percentages may be due in part to the pipeline used to
assign taxonomies.
In part because most GI microorganisms have not
been successfully cultured, little is known about the
phenotype of the GI microbiota in cats. The three major
short-chain fatty acids (SCFA) found in cats are butyrate,
acetate, and propionate (Brosey et al 2000). In particular,
butyrate is considered to play a vital role in colonic human health (Hamer et al 2008, Louis and Flint, 2009) but
little is known about its role in intestinal health of cats.
Nonetheless, butyrate-producing bacteria are commonly
found in feces of cats (Handl et al 2011).
THE GI MICROBIOTA OF DOGS
Stomach. A study published last year showed that the stomach of healthy dogs is home of a diverse microbiota (at
least 4 phyla were identified), as evaluated by 454-pyrosequencing (Garcia-Mazcorro et al 2012a). Despite this
diversity, one single genus (i.e., Helicobacter) was by
far the most predominant (~ 98% of all gastric microbiota). These results are in accordance with one study that
showed that the human stomach is also home of a diverse
microbiota, although the genus Helicobacter (H. pylori
only) constituted only 42% of all sequences analyzed
(Bik et al 2006).
Small intestine. Clapper and Meade (1963) attempted
one of the first characterizations of bacteria and fungi
in the lower intestinal tract of dogs using twelve different types of culture media. Using rectal swabs from 25
healthy Beagle dogs, the authors isolated 20 species of
bacteria and 10 species of fungi (Clapper and Meade
1963). More recent studies using molecular techniques
have shown the presence of at least four different bacterial phyla in the intestinal tract of dogs, namely Firmicutes (47.7%), Proteobacteria (23.3%), Fusobacteria
(16.6%), and Bacteroidetes (16.6%) (Suchodolski et al
2008). Interestingly, these proportions differed depending on the intestinal compartment analyzed, with duodenum and jejunum containing more than 50% Firmicutes, while the ileum and colon only harbored ~ 30%
of this phylum (Suchodolski et al 2008). Still, a more
recent study used 454-pyrosequencing and identified ten
bacterial phyla in the jejunum of dogs (Suchodolski et
al 2009), although more than half of these groups were
GASTROINTESTINAL, MICROBIOTA, CATS, DOGS
only found in very low proportions (< 1% of all microbiota). A recent article used FISH to quantify bacteria in
the duodenal biopsies of dogs and found a median of zero
bacteria (range: 0-3) per microscopic field using almost
1000 microscopic fields (Garcia-Mazcorro et al 2012a).
In contrast, the same article found a high bacterial diversity (median: 173 OTUs) using 454-pyrosequencing also
in duodenal biopsies from the same dogs. The reasons
for this discrepancy are unknown but it may relate to the
destruction of intestinal mucus during formalin fixation
of the biopsies before paraffin embedding.
Large intestine. Some studies suggest that, in faeces,
Firmicutes represent the great majority (> 90%) of the
faecal microbiota in dogs (Garcia-Mazcorro et al 2011,
Handl et al 2011). On the other hand, a recent metagenomic study suggested that the Bacteroidetes/Chlorobi
group and Firmicutes were the dominant bacterial phyla
(~ 35%), followed by Proteobacteria (~ 15%) and Fusobacteria (~ 8%) also in faeces of dogs (Swanson et al
2011). However, the results of this study show, just as in
the reports of Tun et al, that Bacteroidetes only represented ~ 3% of all the obtained reads, while the Chloroflexi
and the Chlorobi groups represented less than 1% of the
reads. Therefore, it is not clear which group represented
the difference between the reported percentage of the
Bacteroidetes/Chlorobi group (~ 35%) and the percentage of Bacteroidetes alone (~ 3%). It is possible that the
remaining percentage represents unclassified members of
Bacteroidetes, but this has been scarcely discussed in the
available literature.
As mentioned above, little is known about the phenotype of GI microorganisms in cats and dogs. As in
cats, the major SCFA in dogs are butyrate, acetate, and
propionate (Swanson et al 2002). A butyrate-producer
bacterium that has attracted much attention for its role in
intestinal health of humans is Faecalibacterium prausnitzii (Sokol et al 2009). A recent article suggests that
Faecalibacterium-relatives are also abundant in faeces
of dogs (Garcia-Mazcorro et al 2012b), although it has
been suggested that canine Faecalibacterium spp. may
not be F. prausnitzii, based on phylogenetic analysis of
near-full-length 16S rRNA gene sequences belonging
to a canine clone and a human strain (Suchodolski et al
2008). Other butyrate-producers bacteria such as Eubacterium and Roseburia have been found in dogs and cats
(Handl et al 2011).
MANIPULATION OF THE GI MICROBIOTA
Acknowledging that GI microbiota is closely involved in the wellbeing of the host led to the idea of manipulating intestinal microorganisms to enhance health. Several approaches have been used to accomplish this goal in
cats and dogs (see below). In contrast, the consumption
of therapeutic agents such as antibiotics can also lead to
unintended modifications of the GI microbiota, although
less research on this topic is available in cats and dogs.
PROBIOTICS AND PREBIOTICS
Probiotics can be defined as live microorganisms that,
if consumed in adequate amounts, would provide a health
benefit to the host (FAO/WHO, 2002). On the other hand,
prebiotics are selectively fermented ingredients that cause specific changes in the composition and/or activity of
the gastrointestinal microbiota (Gibson et al 2010), thus
also conferring health benefits on the host, while synbiotics are preparations containing both probiotics and prebiotics.
Sunvold et al (1995) were among the first to evaluate
the in vitro effect of a prebiotic on faecal fermentation
patterns of cats and dogs. In this study, the addition of fiber (citrus pulp) led to a higher organic matter disappearance and lower acetate to propionate ratio in both dogs
and cats; however, these changes were not correlated
with modification of the faecal microbiota. While other
studies have also researched the properties and effects
of probiotics and prebiotics on the composition and/or
activity of the canine and feline intestinal microbiota in
vitro (Strompfová et al 2004, Cutrignelli et al 2009) and
in vivo (Vanhoutte et al 2005, Biagi et al 2007), most of
these investigations have only studied selected intestinal
bacterial groups, an approach that does not fully assess
the effect of probiotics and prebiotics on the intestinal
microbial ecosystem. A recent study investigated the effect of a commercial preparation of probiotics and prebiotics on the faecal microbial composition of healthy cats
and dogs using several molecular techniques, including a
high-throughput sequencing technique (Garcia-Mazcorro et al 2011). Similarly to other studies, the authors of
this investigation showed that the consumption of the formulation leads to increases in faecal abundance of the ingested microorganisms, a change that rapidly disappears
2-3 days after consumption of the preparation. Interestingly, these quantitative changes in specific bacterial
groups did not seem to lead to major modifications in the
overall phylogenetic composition of the fecal microbiota,
as evaluated by 454-pyrosequencing. This is an interesting observation because probiotics are thought to modulate the intestinal microbiota, including other, unrelated
to the ingested microorganisms, bacteria. This modulation effect of probiotics on the intestinal microbiota has
also been suggested in humans, as evaluated by culture
(Venturi et al 1999) and molecular techniques (Larsen
et al 2011), although the results are also controversial.
For example, one study showed that the consumption
of a synbiotic preparation leads to changes in bacterial
populations but no significant differences in fecal chemistry (Worthley et al 2009), while others propose that the
intake of a synbiotic food leads to modulation of the gut
metabolic activities with a maintenance of gut “biostruc119
JF GARCIA-MAZCORRO Y MINAMOTO
ture” (Vitali et al 2010). The discrepancy among different investigations may be due to the amount and types of
probiotics administered (Pagnini et al 2010), as well as
the combination and potential synergistic effect of different microorganisms. While some researchers encourage
the design and use of several strains and/or species of
microorganisms in probiotic formulations (Timmerman
et al 2004), few data support a more beneficial effect
of these multi-strains/species preparations compared to
single strain preparations.
ANTIBIOTICS
Antibiotics are commonly used in Veterinary Medicine, but concerns have been raised about the potential
reservoir of antibiotic resistance among the native intestinal microbiota of animals (Moyaert et al 2006). While
in humans the effect of antibiotics on the intestinal microbiota has been characterised in depth (Dethlefsen and
Relman 2011), little is known about the effect of antibiotics on the GI microbiota of cats and dogs. Johnston et al
(1999) evaluated changes in duodenal bacteria of cats (n
= 6) during metronidazole treatment, but this study only
used culture techniques. Suchodolski et al (2009) analyzed changes in the small intestinal microbiota of dogs (n
= 5) during administration of tylosin using 454-pyrosequencing. In this study, several changes in the abundance
of different bacterial groups were observed, including an
increase in the proportions of Enterococcus spp. which
have been reported to be resistant to tylosin. However,
these changes in bacterial amounts were not accompanied by any obvious clinical effect. Grønvold et al (2010)
studied changes in faecal microbiota of healthy dogs (n
= 7) during administration of amoxicilin using DGGE
and qPCR. In this study, most of the variation in DGGE
band profiles could be attributed to dog-specific factors,
suggesting a minimal change in the composition of the
fecal microbiota, as determined by the employed techniques.
INHIBITORS OF GASTRIC ACID SECRETION
Gastric acid is one of the first physiological barriers
to impede the passage of potentially harmful agents into
the intestinal tract. It is believed that inhibitors of gastric acid secretion can change the composition of the
GI microbiota (Heidelbaugh et al 2009, Lombardo et al
2010), but only few studies support this statement and
have mainly used culture techniques to study specific microorganisms (e.g. Helicobacter pylori). A recent study
used a combination of several molecular techniques and
concluded that the proton-pump inhibitor omeprazole
can change the quantitative abundance of several gastric,
duodenal and faecal microorganisms in healthy dogs, a
change that did not seem to lead to major shifts in the
overall phylogenetic composition of the gastric and small
120
intestinal microbiota (Garcia-Mazcorro et al 2012a). Interestingly, the observed effect of omeprazole on the canine GI microbiota was dependent on the gender of the
animals, perhaps suggesting a distinctive metabolism of
the drug in male and female dogs.
CONCLUDING REMARKS
The GI tract of cats and dogs harbors a complex microbiota. The study of GI microorganisms is of interest
because of its close relationship with the wellbeing of
the host. Also, an increasing number of investigations
suggest that GI microorganisms may play a role in the
etiology of various GI disorders. However, little is known
about what represents a healthy microbiota, its normal
biological variations within and among individuals, and
how to successfully manipulate it to prevent or treat GI
disease. In order to achieve this goal, future collaborative studies should complement phylogenetic characterizations of the GI microbiota with functional (metabolic)
analyses.
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